1. Field of the Invention
The present invention relates to a communication system, and more particularly, to a method and apparatus for signal reception in a Multiple Input Multiple Output (MIMO) communication system.
2. Description of the Related Art
An efficient way of providing increased throughput of a modern wireless communication system is to use multiple transmit and receive antennas (MIMO—Multiple-Input-Multiple-Output), that is, more than one transmission channel—transmit antennas and more than one reception channel—receive antennas are used.
A set of signal propagation channels between the transmit and the receive antennas is called a MIMO channel. Throughput increase in MIMO systems is achieved by parallel transmission of different data over different spatial channels. In the case most important in practice the instant MIMO channel implementation can be estimated at the receive end but this estimate is unavailable at the transmit end.
Due to lack of information about the MIMO channel state at the transmit end, there is no possibility to optimize information transmission over parallel spatial channels, which results in generation of mutual interference at the receive end by signals transmitted through different antennas.
The most widespread transmission technique in MIMO systems is spatial multiplexing.
According to spatial multiplexing technique, coding, interleaving and modulation of the initial binary symbol (or bit) stream are performed in the transmitter, thus forming a stream of the modulation symbols s, each representing L sequential bits of the initial stream and belonging to the set 2L of various values. This stream is divided into packets. The number of symbols in each packet corresponds to the number of transmit antennas N and the entire packet is transmitted at once, i.e. one modulation symbol via each antenna.
Reception is performed by means of M≧N receive antennas. A set of signals of the receive antennas is usually represented by an M-dimensional vector which can be expressed as a linear combination, as set forth in Equation (1):x=H·s+n  (1),
where s=[s1, . . . sN]T, x=[x1, . . . xM]T are the vectors of the transmitted and received signals, respectively, H is channel matrix, whose elements hi,j represent complex transfer coefficients from the j-th transmit to the i-th receive antenna, n=[n1, . . . nM]T is the receive antenna additive Gaussian noise vector, [.]T is the sign of transposition.
When receiving such a multi-dimensional signal, channel matrix H is primarily estimated and then the symbols of vector s are demodulated using this estimate.
Therefore, the efficiency of the spatial multiplexing approach is determined by the receive algorithm efficiency, i.e. its ability to restore signals transmitted over parallel spatial channels taking into account their interference and additive noise.
The most efficient multi-dimensional signal reception algorithm is a Maximum Likelihood Algorithm (MLA).
For example, this method considers all possible combinations of simultaneously transmitted information symbols (all possible values of the vector s) and such value of vector s is selected, which provides the decision function minimum. Squared norm of the difference between the vector x of the received signals and the vector s is transformed by premultiplication by the channel matrix H. Therefore, the set of estimates of the transmitted packet s of symbols can be expressed by the vector z, as set forth in Equation (2):
                              z          =                                                    a                ⁢                                                                  ⁢                r                ⁢                                                                  ⁢                g                ⁢                                                                  ⁢                min                                            s                ∈                A                                      ⁢                                                                            x                  -                  Hs                                                            2                                      ,                            (        2        )            
where A is the set of all kinds of the vector s.
However, MLA implementation is very complex and its complexity increases exponentially with the growing number of transmit antennas and the number of information bits transmitted through each antenna.
Therefore, on practical grounds linear reception methods are more attractive when the transmitted symbol vector estimate is expressed through linear transformation of the received signals vector x. Such reception methods are described in Robust Linear MIMO Receivers.
In such methods, the linear transformation coefficients are formed so as to optimize the estimate according to some criterion. The most efficient linear method is the Minimum Mean Squared Error (MMSE) algorithm.
Linear algorithms produce lower performance than the maximum likelihood method. Therefore, a Successive Interference Cancellation (SIC) method is often used along with linear reception methods. Also, in well-known Ordered Successive Interference Cancellation (OSIC) algorithms, each symbol of the packet s is estimated successively given the interference caused by other symbols is rejected or reduced.
When receiving using MMSE method with OSIC (MMSE-OSIC), the channel matrix H is estimated and the order of estimating the symbols of vector s is determined so as to first estimate the symbols least distorted by the propagation channel. Usually the ordering criterion is the values of the matrix H vectors-columns norms. Next, the first symbol estimate is formed by MMSE. This estimate is quantized, that is, the modulation symbol closest to the estimate is determined in the modulation map. Then the contribution of the symbol is excluded from the input symbol vector by subtracting the result of converting the symbol value by the propagation channel. Further, using this “purer” input signal and the estimate of the channel matrix, modified by ordering and excluding the first column, the MMSE estimate is generated for the next symbol of the vector s. The procedure is repeated for all symbols of the vector s.
The OSIC procedure allows for the gain in the MIMO signal reception performance. This gain, however, depends greatly on using coding. Thus, in terms of performance before the decoder, MMSE-OSIC provides high gain compared to MMSE. This gain remains to some extent when convolutional coding and hard decoding are used. However, when soft decoding is used, the MMSE-OSIC either does not provide any gain or leads to a loss.
This example underlines another important aspect of the design of the MIMO algorithm. That is, any MIMO algorithm should be properly combined with other signal processing algorithms in the communication system and first of all with a forward error correction coding algorithm.
During reception, the produced symbol estimates z are usually demodulated, converted to a binary form and applied to the decoder to restore the initial data stream. The most efficient decoding is soft decoding. Soft bit estimates (decisions) are applied to the decoder in the form of {B −B}, where the sign corresponds to the hard estimate of the transmitted bit 1 or −1, and the absolute value B is a metric representing the probability that the bit takes the hard value.
A soft decision of some bit bk is the Log-Likelihood Ratio (LLR). With no a priori information about the transmitted bit values available and provided the transmitted bits are mutually independent, the LLR for the k-th bit of the transmitted vector s can be expressed with high approximation degree as set forth in Equation (3):
                                          LLR            k                    =                                    1                              2                ⁢                                  σ                  2                                                      ⁢                          (                                                                    min                                          s                      ∈                                              A                        k                                                  (                                                      -                            1                                                    )                                                                                                      ⁢                                                                                                          x                        -                        HS                                                                                    2                                                  -                                                      min                                          s                      ∈                                              A                        k                                                  (                          1                          )                                                                                                      ⁢                                                                                                          x                        -                        HS                                                                                    2                                                              )                                      ,                            (        3        )            
where Ak(1) and Ak(−1) are the sets of the vector s values for which the k-th bit takes on the values 1 and −1, respectively, ∥.∥ is the vector norm.
Forming of metrics by this expression significantly complicates the reception algorithm, because the number of elements of the sets Ak(1) and Ak(−1) increases exponentially with the increased number of antennas and modulation constellation size.
Linear methods and SIC based methods allow for the simplified soft decision generation, which is performed for each symbol individually using the obtained symbol estimate and the modulation map.
There is also a decoding method and apparatus having low complexity and high performance in a communication system using multi-dimensional signaling, in which the transmitted symbols are estimated by some suboptimal method and the soft decisions are generated. Then a reduced search set, comprising the transmitted symbols vectors, corresponding to all kinds of the least reliable bits combinations, is generated. Then new soft decisions are generated based on the reduced search set and the transmitted signals vector.
The MMSE method using the suboptimal algorithm provides the best tradeoff between implementation complexity and performance when soft decoding is concerned. In addition, the efficiency of such soft decisions generation is relatively low. Computer simulation shows that given the small amount of reduced search, the performance of this method is even lower than that of the MMSE. To obtain significant gain, this set should be increased, which essentially complicates the algorithm.